Answers from your own documents — with a citation.
Retrieval-augmented generation grounds an assistant in your private content, so it quotes the right page instead of guessing — and tells you where the answer came from.
A RAG platform knows your policies, not the internet's.
A raw language model answers from whatever it was trained on. That is fine for trivia and dangerous for your business — it will state an old price, a policy you retired, or a number it invented, all with total confidence. Retrieval fixes the root cause: the model reads the relevant passage from your own documents first, then answers from it.
We build the whole pipeline — clean chunking, embeddings, a vector store, grounded prompts and a freshness loop that re-indexes as your content changes. Every answer can carry a citation back to the source paragraph, so a reader verifies it in one click rather than taking it on faith.
- Chunking tuned to your documents, so a retrieved passage is a whole thought, not half a sentence
- Embeddings and a vector store sized to your corpus and your budget
- Grounded prompts that answer from the retrieved passage and cite the source
- A freshness loop that re-indexes when a document changes, so answers never go stale
The whole pipeline, not a wrapper.
Retrieval quality is decided long before the model speaks — in how you chunk, index, ground and refresh. That is the work we do.
Document ingestion & chunking
Your content arrives as PDFs, wikis, spreadsheets and web pages, each messy in its own way. We normalise and chunk it so every retrievable piece is a coherent, self-contained passage. It is the single biggest factor in whether retrieval returns the right thing.
Embeddings & vector store
We turn your passages into embeddings and store them in a vector database sized to your corpus. The right index, similarity metric and metadata mean the model finds the relevant page fast, even across tens of thousands of documents.
Grounded prompts & citations
The prompt is built so the model answers from the retrieved passage and nothing else, and returns the source alongside the answer. A reader can click through to the exact paragraph — which is what turns a plausible answer into a trustworthy one.
Freshness & re-indexing
Policies change, prices change, documents get replaced. We wire in a freshness loop that re-indexes on a schedule or on edit, so the assistant answers from today's version — not the one you uploaded six months ago.
Access-aware retrieval
Not everyone should see everything. Retrieval respects roles and permissions, so a user only gets answers from documents they are allowed to read. Grounding and access control are the same system here, not bolted on afterwards.
Evaluation & anti-hallucination
We test retrieval against real questions and hard edge cases, measure whether the cited source actually supports the answer, and tune until it does. Where your documents genuinely do not hold the answer, the assistant says so instead of filling the gap.
Fewer confident wrong answers.
The point is not a cleverer chatbot — it is answers your team and customers can actually rely on.
Grounding first, then generation.
We spend the effort where retrieval is actually won — on the content, the index and the citations.
The corpus
We inventory the documents that hold your answers, and agree what is authoritative and what to ignore.
Prep the content
Normalise and chunk the sources into clean, self-contained passages — the work that decides retrieval quality.
Embed & store
Generate embeddings and load a vector store sized to your corpus, with metadata for filtering and permissions.
Prompt & cite
Build grounded prompts that answer from retrieved passages and return a citation for every claim.
Test & refresh
Measure whether citations support answers, tune retrieval, and wire the freshness loop before go-live.
Retrieval inside a real assistant.
A RAG platform is the engine; it ships inside the chatbot, voice agent or app we build around it.
Things you might be wondering.
What is RAG, in plain terms?
How does it stop the AI making things up?
What happens when our documents change?
Can it respect who is allowed to see what?
A pile of documents your team keeps answering from?
Point us at the content. We will build retrieval that answers from it — grounded, current and cited.